The present invention relates to control systems for an active, adaptive vibration and noise attenuation system (AAVNAS). The present invention serves as the intelligence of an overall system that has several parts. Generally, the other parts of the noise control system are called the plant and would include the noise producing system itself, sensors for measuring the objectionable vibration and noise, a mechanism for altering the production of noise and vibration, and some parameter which can be measured independently of the noise and vibration which is related to the noise and vibration production and can serve as an element in the accurate estimation of noise and vibration. In particular, the present invention relates to a control system using neural networks to emulate and control the noise and vibration characteristics of a nonlinear plant.
Virtually all dynamic mechanical devices produce vibration which, when transmitted through air within audible frequency ranges, is recognized as audible noise. Both the original vibration and the resultant audible noise have undesirable effects. Vibration in machinery can damage or degrade the machinery""s performance. Noise and vibration perceived by people in the vicinity of the machinery may distract those people and cause fatigue or other physical maladies. Consequently, a need exists for systems and techniques which can be used to reduce noise and vibration.
Efforts to control noise and vibration can be classified into two categories: passive and active. Passive noise control techniques are distinguishable from active in that passive techniques are arranged to absorb energy from the plant or, by reason of tuned mounts, isolate the vibrating machinery and thus do not add any energy to the plant, i.e. the system being controlled. Many prior attempts to control noise and vibration utilized passive techniques such as mufflers or sound-absorbing insulation. However, passive noise and vibration control techniques approach practical limits in terms of cost and many other characteristics versus effectiveness. Further significant reductions in noise and vibration levels usually require advances in the state-of-the-art active control technology.
Active techniques seek to analyze the noise and vibration that the plant produces and then reduce the effects by either actively altering the characteristics of the system or by inducing acoustic wave interference accomplished by emitting noises/vibrations at specific time-delayed and phased-reversed frequencies in order to cancel out the noise and vibration from the plant. A more detailed explanation of the physics behind noise cancellation is given in an article entitled xe2x80x9cA Primer on Active Sound and Vibration Controlxe2x80x9d written by Larry J. Eriksson which appeared on page 18 of the February, 1997, issue of xe2x80x9cSensorsxe2x80x9d.
One method known in the art is to measure the noise and vibration disturbances at locations where cancellation is desired and to feed this information back into an active controller which then makes alteration/cancellation adjustments to reduce the noise and vibration disturbances. Feedback systems tend to be effective when the time delay through the controller actuator and sensors is kept to a minimum.
Another method is to place a reference sensor as close as possible to the vibration/noise disturbance producing source in addition to the measurements of the noise and vibration disturbances at locations where cancellation is desired. Such a sensor is referred to as a reference sensor and allows the use of feed-forward algorithms. Feed-forward algorithms such as the Filtered-X Least Mean Squares (LMS) algorithm minimize the measured disturbance signals using a gradient descent algorithm to adapt the coefficient of a FIR (Finite Impulse Response) filter. With feed-forward systems, the FIR filter coefficients are updated so that the transfer function from the disturbance source to the disturbance signals where cancellation is desired, is equal to the net transfer function from the source through the reference sensor, FIR filter, and actuator to the same disturbance signals. The adaptive algorithm computes a FIR filter that best equalizes these two paths. U.S. Pat. No. 5,332,061 issued to Kamal Majeed, on Jul. 26, 1994, discloses one such system used to attenuate vibrations in a vehicle generated by an internal combustion engine. These algorithms are effective when the reference sensors are coherent with the error signals and have a small time delay with respect to the source and when the system controlled is linear. The Filtered-X LMS algorithm is described in the textbook xe2x80x9cAdaptive Signal Processing by Bernard Widrow and Samuel Stearns (copyright) 1985, Prentice-Hall Inc., ISBN: 0-13-004029-0xe2x80x9d.
Few practical implementations of nonlinear active controllers have been realized. Nonlinear active control systems are required when the actuators and/or the plant exhibit nonlinear dynamics. Neural networks are one method known in the art to model and control the behavior of nonlinear systems. A neural net plant emulator is first trained to identify the nonlinear plant behavior. Then, the neural net controller is trained in real time using the results of the emulator to control the actual noise and vibration disturbance signals. There are many publications on the training of neural nets such as backpropagation (with or without momentum), conjugate gradient, quasi-Newton algorithms and nonlinear Kalman Filtering. One such publication is Neural Networksxe2x80x94A Comprehensive Foundation by Simon Haykin, (copyright) 1994, Macmillan Publishing Company, ISBN: 0-02-352761-7.
U.S. Pat. No. 5,434,783, issued to Chinmoy Pal, on Jul. 18, 1995, discloses a system incorporating neural networks for use in canceling noise and vibration in an automobile. The Pal patent discloses a system using two neural networks. The xe2x80x9cidentificationxe2x80x9d neural net models the behavior of the plant being controlled. The xe2x80x9ccontrollerxe2x80x9d net calculates the actuator command signals to reduce the automobile interior noise and vibrations of the vehicle body panel. The neural net architecture proposed in this patent includes feedback of the outputs from the neural net (controller or emulator) through ARMA (Auto-Regressive-Moving-Average) models to capture the temporal dynamic behavior of the plant. The output filtered signals from the ARMA models are then used as the inputs to the neural nets. In addition, feedback coupling exists from the emulator output, also filtered by alternate ARMA models to the controller inputs.
U.S. Pat. No. 5,386,689, issued to Daniel J. Bozich, on Feb. 7, 1995, discloses a system similar to Pal, utilizing dual neural networks to control actively vibration in gas turbine engines. This patent discloses the use of two neural nets, an emulator and a controller, to reduce the vibration and noise generated by a gas turbine engine, using actuators and sensors. The emulator in the Bozich patent is used to provide compensation to the neural net controller by using an idea similar to the Fx-LMS algorithm. A reference signal is passed in a feed-forward manner through the emulator to provide a filtered reference signal, which is then used to update the neural net controller weights. The Fx-LMS approach, and hence the approach of the Bozich patent, both assume that the plant (as represented by the emulator) and the controller are interchangeable and this in general is true for linear systems and may be possibly applicable for moderately linear systems.
The flow of a fluid over a surface is one situation in which noise and vibration can occur. Specific examples of this are the blades of a rotorcraft spinning through the air or the blades of a propeller or impeller spinning in water.
A substantial body of research into the noise and vibration generated by helicopter rotors exists. A helicopter emits a substantial amount of noise as it flies over an area. Noise and vibration levels within the helicopter cabin and throughout the airframe can also be significant. The external noise radiated from a helicopter can be generally classified into three areas: loading, thickness and blade-vortex interaction (BVI).
Loading noise results from the rotation of the blades that are creating lift to keep the helicopter airborne. This rotational movement of lift generates noise that propagates perpendicular to the rotor plane, namely down toward the ground directly beneath the helicopter.
Thickness noise also results from the blades rotating around the main rotor shaft, but in contrast is independent of the lift on the blades. Thickness noise results from the pressure disturbances created as a blade passage causes the air to displace and then return to its initial state. The thicker the blades, the more displacement, thus the term thickness noise. These air displacements result in pressure fluctuations that result in radiated noise. Thickness noise radiates in the plane of the rotor, and thus projects ahead of and behind the helicopter. The blade velocity (as noted by the Mach number) also impacts the amount of thickness noise, the faster the blades motion, the greater the noise generated. As the relative speed of the air displacing around the blade approaches the speed of sound (sonic or Mach number=1.0), the magnitude of noise rises sharply (i.e. high-speed thickness noise). When regions of the displaced airflow exceed sonic velocities, the flow is referred to as delocalized, and a great increase in sound levels and radiation is observed. In this situation, the noise is referred to as High Speed Impulsive (HSI) noise. The highest velocity flow is observed on the advancing side of the rotor, and thus the highest thickness noise levels propagate ahead of the helicopter.
As used in this application, HSI noise should be understood to also cover the more general thickness type of noise which may or may not occur at the time that HSI noise occurs. Since the highest Mach numbers are on the advancing side of-the rotors, these noise sources propagate ahead of the aircraft and can result in the helicopter""s detection over a battlefield by threat acoustic sensors and mines. Reductions in blade thickness and rotor RPM, thus Mach number, can lead to significant reductions of the noise levels that propagate forward of the helicopter in the plane of its rotor blades, thus reducing the detectability range of the helicopter. However, reductions in blade thickness and tip speed also degrade rotor performance. The addition of active control technology to a rotor with reduced blade thickness and lower rotor RPM may restore or improve the helicopter""s performance while yielding significant reductions in noise propagation and thus detection distances.
BVI noise is related to the close interaction of the main rotor blades with the wake vortex elements generated at the ends of the spinning main rotor blades. These interactions increase the far-field ground noise emanating from the helicopter. BVI noise dominates the noise levels when the helicopter is descending or when the helicopter is flying in a terrain-following flight pathxe2x80x94sometimes referred to as nap of the earth (NOE) flight profiles. BVI noise is predominantly directed down toward the ground, i.e., perpendicular to the plane of the rotor blades. Such ground noises are undesirable in civilian contexts because they are objectionable to populace in the surrounding area. Such ground noises are also undesirable in military contexts because the noise makes the helicopter more detectable, which makes the helicopter more vulnerable to enemy action. In a military context; a reduction in BVI noise levels during descent or terrain-following flight would greatly enhance the success of missions requiring flight at low altitude in order to evade radar detection, while avoiding detection by acoustic sensors which might trigger anti-helicopter, explosive mines.
These wake vortex elements that lead to BVI noise are also part of a more complex rotor wake structure that varies in time and space over the entire rotor disk. This entire wake structure is also responsible for generating vibratory loads on the rotor system which are transferred through the blades and hub into the airframe and result in undesirable vibrations in the helicopter cockpit and cabin areas.
Open loop aerodynamic simulations have shown that actuating a trailing edge flap on each rotor blade to introduce flap motions at xe2x80x98N per revxe2x80x99 can reduce BVI noise and vibration. For example, a 2 per rev harmonic signal is twice the speed of rotation of the main rotor of the aircraft and for a rotor rotating at five revolutions per second (300 RPM), the 2 per rev is a 10 Hz harmonic input signal. The flap is typically driven with a superposition of 2 per rev through 5 per rev harmonic input signals. One published paper (xe2x80x9c23rd European Rotorcraft Forum in Dresden, Germany, Sep. 16-18, 1997: Individual blade control by servo-flap and blade root control-a collaborative research and development programmexe2x80x9d, written by D. Schimke, P. Janker, A. Blaas, R. Kube, G. Schewe, Ch. Kebler), describes the use of superimposed harmonics of a base frequency, P, of vibration and sound used to effect cancellation of that vibration and sound. The superimposed harmonics include the 2P through 5P harmonics.
Researchers have also determined that a pressure transducer located on the leading-edge of the blade, at an appropriate blade span location, would correlate well with the far-field noise. One such publication (xe2x80x9c19""th European Rotorcraft Forum, Cenobbio, Italy, Sep. 14-16, 1993: Experimental Results of the European Helinoise Aeroacoustic Rotor Testxe2x80x9d, by W. R. Splettstoesser, G. Niesl, F. Cenedese, F. Nitti, D. G. Papanikas) presents experimental results correlating blade pressure and accoustic characteristics. Experimental noise data in this reference is further evaluated in terms of bandpass levels from 2P-10P comprising the thickness and high speed noise and 6P-40P comprising BVI. Therefore, the output of such a transducer can be used as a noise error sensor for a closed-loop controller for controlling flap position. The aforementioned Schminke paper also discusses the use of pressure transducers at the rotor blades, and the use of wavelet transformation filters to extract the BVI noise signature for closed loop control.
U.S. Pat. No. 5,588,800, issued to Bruce D. Charles, on Dec. 31, 1996, discloses a system for manipulating a flap on a rotor blade to reduce the noise and vibration generated by the rotor. This system alters the flaps in predetermined manners based on the relative angular position of the rotor blade at any given time. The Charles control system is active to the extent that it varies its output depending on the rotor blade position, but does not include a controller to provide real time learning. Without such a controller, the Charles system is unable to optimally adapt its flap control manipulations and provide maximum reduction of noise and vibration emissions based on actual measurements of noise and vibration.
The prior art lacks a comprehensive scheme for actively, adaptively controlling nonlinear noise and vibration by estimating and measuring what noise and vibration outputs will occur based on stimuli that relate to the generating noise and vibration source and then adapting the controlling mechanism to reduce the plant measured noise and vibration disturbances at the desired locations.
The present invention supplies the control system necessary to model the plant, estimate and measure noise and vibration states, direct the alteration of noise-vibration-generating mechanisms, evaluate its noise-vibration-eliminating performance and adjust its directions based upon detected errors in its plant model.
The present invention is a noise and vibration control system associated with a plant which includes one or more portions of mechanical equipment involved in producing or measuring noise and vibration. The present invention filters and quantifies the noise and vibration generated by the plant. The control system incorporates an emulator neural network used to model the relationship between the quantified noise and vibration measurements and one or more stimuli related to the plant generating the noise and vibration. A second neural network, the controller, uses a reference signal to generate a noise and vibration correction signal which is passed to some means for altering the noise and vibration generated by the plant. The emulator then measures the effectiveness of the correction signal in reducing noise and vibration. The emulator calculates the gradient of the error in its plant model. This gradient is passed to the controller to adapt the calculations used to generate the correction signal. Over time, the parameters within the controller are adapted to produce an optimal adjustment signal reducing the noise and vibrations generated by the plant. The input signals to the emulator and controller are stored in a plurality of time-based filters.
One specific application of the present invention is to control the noise and vibration produced by the blades of a rotorcraft. The noise and vibration is measured by sensors mounted on the rotor blades and throughout the rotorcraft. These sensed signals are then filtered and quantified. An emulator neural network models the relationship between the quantified signals and various stimuli related to the motion of the rotorcraft including the angular position of the rotor relative to the body and the forward velocity of the rotorcraft. A controller neural network uses a reference signal and the current flight regime of the rotorcraft to generate an adjustment signal. This adjustment signal controls flaps located on the blades of the rotorcraft. By adjusting these blade flaps as the blade rotates around the rotor, the controller is able to alter the flow of air across the blades and, thus, alter the noise and vibration generated by that airflow across the blades. The emulator neural network estimates the noise and vibrations resulting from the adjustment signals. These estimates are compared against actual noise and vibration measurements to develop a error gradient in the plant model. This error gradient is then used to adapt the parameters that the controller neural network used to generate the original adjustment signal. As this adjustment occurs during each execution cycle of the controller and emulator, the controller neural network is adapted toward parameters which result in adjustment signals minimizing the noise and vibrations generated by airflow across the rotor blades.